PhD Chapter 2
Results
This series of files compile all analyses done during Chapter 2.
All analyses have been done with R 4.0.2.
Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it
1. Maps
1.1. General map
Stations considered for this Chapter:
1.2. Parameters maps
Maps of functional traits density:
Body: non-calcified tissue
Body: calcareous
Body: calcium carbonate
Body: amorphous calcium carbonate
Body: aragonite
Body: calcite
Body: high magnesium calcite
Body: chitinous
Size: small
Size: medium
Size: large
Food: filter feeders
Food: surface deposit feeders
Food: subsurface deposit feeders
Food: grazers
Food: predators
Food: scavengers
Food: parasites
Mobility: sessile
Mobility: limited
Mobility: mobile
Lifestyle: fixed
Lifestyle: tubicolous
Lifestyle: burrower
Lifestyle: crawler
Lifestyle: swimmer
2. Rank-Frequency diagrams
We drew Rank-Frequency diagrams to study the structure of communities when considering taxa frequencies.
3. Indicators of ecosystem status
This section tests different indicators to reflect the environmental status in Baie des Sept Îles. We will consider classic methods, such as community characteristics, with functional diversity indices and other techniques. We will look at their results critically to see which could be the best for which situation.
When relevant, we used the five classes based on Environmental Quality Ratios established for the WFD and MSFD (which varies between 0 and 1):
- 0 = bad (red #FF0000)
- poor (orange #FFA500)
- moderate (yellow #EEEE00)
- good (green #228B22)
- 1 = high (blue #0000EE)
Reference values and limits for each class are specific to each indicator.
3.1. Richness
3.1.1. Methodology
We calculated a basic community characteristic, the specific richness, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 has been performed for the considered stations.
ASSUMPTION: A higher richness indicates a high status without perturbation.
3.1.2. Application
When we considered the data without a distinction by station, the global specific richness is 132.
3.1.3. Ecological Quality Status
3.2. Total density & biomass
3.2.1. Methodology
We calculated basic community characteristics, the total density and biomass of individuals, to see if patterns could be detected in the study area. The same calculations as for Chapter 1 has been performed (with the addition of biomass data) for the considered stations.
3.2.2. Application
When we considered the data without a distinction by station, global total density is 10,915 individuals.grab-1 and global total biomass is 936.6919 gWM.grab-1.
3.3. Diversity
3.3.1. Methodology
We calculated basic a community characteristic, the Shannon diversity, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 has been performed for the considered stations.
ASSUMPTION: A higher diversity indicates a high status without perturbation.
3.3.2. Application
When we considered the data without a distinction by station, the global Shannon diversity is 3.251545.
3.3.3. Ecological Quality Status
3.4. Evenness
3.4.1. Methodology
We calculated a basic community characteristics, the Pielou evenness, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 has been performed for the considered stations.
3.4.2. Application
When we considered the data without a distinction by station, the global Pielou evenness is 0.6659178.
3.5. Taxonomic diversity
3.5.1. Methodology
We calculated a basic community characteristic, the taxonomic diversity, to see if patterns could be detected in the study area. The same calculations as for Chapter 1 has been performed for the considered stations.
3.5.2. Application
When we considered the data without a distinction by station, the global taxonomic diversity is 74.16541.
3.6. Functional diversity
3.6.1. Methodology
We studied functional diversity based on five biological traits and 26 modalities:
- body composition (non calcified tissue, calcareous, calcareous calcium carbonate, calcareous amorphous calcium carbonate, calcareous aragonite, calcareous calcite, calcareous high magnesium calcite, chitinous)
- body size (small, medium, large)
- food diet (filter, surface deposit, subsurface deposit, predator, scavenger, grazer, parasite)
- mobility (sessile, limited, mobile)
- lifestyle (fixed, tubicolous, burrower, crawler, swimmer)
Species were assigned a value for each modality using a scale varying from 0 (absence of the modality) to 1 (presence). All where the sum of the values for every modality of a trait equals 1. This allowed to calculate functional richness, divergence and evenness according to Laliberté & Legendre (2010).
3.6.2. Application
For some reason, R is not able to calculate a global value.
3.7. AZTI Marine Biotic Index (AMBI)
3.7.1. Methodology
AMBI (also called biotic coefficient) is an ecological index that is used to detect a perturbation in an ecosystem based on the composition of the communities (Borja et al., 2000). This perturbation is linked with an organic matter increase, according to Pearson and Rosenberg (1978)’s model.
To compute the index, species are classed into five groups in relation to their tolerance to this perturbation:
- group I (GI): vulnerable species
- group II (GII): indifferent species
- group III (GIII): tolerant species
- group IV (GIV): first-order opportunistics
- group V (GV): second-order opportunistics
These groups are based on expert opinion on the physiology of species and experimental studies, but the attribution of a species to a group can be somewhat arbitrary (e.g. based on related phyla information) so it needs to be interpretated carefully.
AMBI is continuous between 0 to 6, and is calculated using this equation:
\[ AMBI = \frac{\sum_{i}^{GI-V} w_{i} . P_{i}}{100} \]
- \(P_{i}\) is the proportion of each group (percentage of the total density of species)
- \(w_{i}\) is the weighting parameter of each group (respectively 0, 1.5, 3, 4.5 and 6)
- \(i\) is the ecological group
ASSUMPTION: Sensitive species are only present in pristine ecosystems, while the dominance of opportunists indicates a perturbed state.
3.7.2. Application
When we considered the data without a distinction by station, the global AMBI index is 1.543.
3.8. Multivariate AMBI (M-AMBI)
3.8.1. Methodology
M-AMBI is a complementary method that is used to calculate an Ecological Quality Ratio (EQR), a measure of the good environmental status. It is based on a multivariate ordination of the stations using the AMBI index, the species richness and the Shannon diversity. The result gives a value between 0 and 1 after comparison to reference values.
These values are called “references” but this needs to be discussed as this vision is limited. They have been set with the 95 % percentile of the distribution. This is a recommendation by Nicolas Desroy, so that we do not detect an increase of EQR when there is a small perturbation (see work by Pearson & Rosenberg and the Intermediate Disturbance Hypothesis).
This calculation yielded 21 for S and 2.53 for H.
M-AMBI is continuous between 0 and 1, and is calculated using a dedicated software.
ASSUMPTION: A high richness, high diversity and low AMBI index indicate a high status without perturbation.
3.8.2. Application
As we do not historical data or other reference systems, it is not difficult to calculate M-AMBI here.
3.8.3. Ecological Quality Status
No clear tendancy can be discovered here, apart from the fact that the overall status seems to be “High”. Several hypothesises can explain this result:
- the M-AMBI index describes reality well, so that overall perturbation from organic matter is low
- there is a bias in the index due to the species classification in groups, originally suited for European ecosystems
- the assumptions for the reference values are not correct
- the configuration of the bay makes the perturbation small relative to the water volume and bathymetric condition
Further work is needed to determine the individual responses of somes species, along with the use of different methods to understand other perturbations and cumulative impacts.
3.9. BENTIX
3.9.1. Methodology
BENTIX is an index based on the same theory as the AMBI, where species are placed in groups based on their tolerance to perturbation (Simboura & Zenetos, 2002). Here also, this perturbation is principally linked to organic matter increase, but two groups only are present:
- GS: species that are sensitive or indifferent to a perturbation (~ AMBI groups I and II)
- GT: species that are tolerant to a perturbation and opportunists (~ AMBI groups III to V)
BENTIX is continuous between 2 and 6 (0 when the habitat is azoic, thus considered highly perturbed), and is calculated using this equation:
\[ BENTIX = \frac{(6 . P_{GS}) + (2 . P_{GT})}{100} \]
- \(P_{GS}\) is the proportion of sensitive species (percentage of the total density of species)
- \(P_{GT}\) is the proportion of tolerant species (percentage of the total density of species)
ASSUMPTION: Sensitive species are only present in pristine ecosystems, while the dominance of opportunists indicates a perturbed state.
3.9.2. Application
When we considered the data without a distinction by station, the global BENTIX index is 5.213659.
3.9.3. Ecological Quality Status
3.10. Benthic opportunistic polychaete/amphipod ratio (BOPA)
3.10.1. Methodology
BOPA is an index that uses a relative abundance ratio of species in a community to infer a state of perturbation. Ratios with many species have been tested, and opportunistic polychaetes and amphipods have been selected to be the most pertinent (originally to detect effects of an oil-spill on soft-bottom communities, e.g. from the Sea Empress or the Amoco Cadiz). It has been updated from its original form in 2000.
BOPA is continuous between 0 and \(log_{10}(2)\) (~ 0.3), and is calculated using this equation:
\[ BOPA = \left( \frac{f_{P}}{f_{A} + 1} + 1 \right) \]
- \(f_{P}\) is the relative frequency of opportunistic polychaetes (abundance / total density)
- \(f_{A}\) is the relative frequency of amphipods (abundance / total density)
We considered AMBI groups GIII to GV for polychaetes and GI for amphipods (without Jassa genera).
ASSUMPTION: Dominance of amphipods characterizes pristine ecosystems, while a dominance of opportunistic polychaetes indicates a perturbed state.
3.10.2. Application
These are the polychaetes and amphipods present in our species list (including the confidence score used during group classification).
| taxon_name | group | confidence_score |
|---|---|---|
| arcteobia_anticostiensis | II | 2 |
| axiothella_catenata | I | 2 |
| bipalponephtys_neotena | II | 3 |
| chone_sp | II | 2 |
| cistenides_granulata | II | 3 |
| cossura_longocirrata | IV | 3 |
| eteone_sp | III | 2 |
| euchone_sp | II | 2 |
| glycera_capitata | II | 3 |
| glycera_sp | II | 2 |
| goniada_maculata | II | 3 |
| harmothoe_sp | II | 2 |
| hediste_diversicolor | III | 3 |
| lumbrineridae_spp | II | 2 |
| maldane_sarsi | II | 3 |
| maldanidae_spp | I | 2 |
| neoleanira_tetragona | II | 3 |
| nephtyidae_spp | II | 2 |
| nephtys_caeca | II | 3 |
| nephtys_incisa | II | 3 |
| nephtys_sp | II | 2 |
| ophelia_limacina | I | 3 |
| opheliidae_spp | I | 2 |
| pholoe_longa | II | 2 |
| pholoe_sp | II | 2 |
| polynoidae_spp | II | 2 |
| praxillella_praetermissa | III | 3 |
| sabellidae_spp | I | 2 |
| scoletoma_fragilis | II | 3 |
| scoletoma_sp | II | 2 |
| scoloplos_sp | I | 2 |
| taxon_name | group | confidence_score |
|---|---|---|
| aceroides_aceroides_latipes | II | 3 |
| ameroculodes_edwardsi | I | 3 |
| ampelisca_vadorum | I | 3 |
| amphipoda | not_assigned | 0 |
| anonyx_lilljeborgi | II | 3 |
| bathymedon_longimanus | II | 3 |
| bathymedon_obtusifrons | II | 3 |
| byblis_gaimardii | I | 3 |
| caprella_septentrionalis | II | 3 |
| crassicorophium_bonellii | III | 3 |
| guernea_prinassus_nordenskioldi | III | 1 |
| hardametopa_carinata | II | 1 |
| ischyroceridae_spp | II | 2 |
| ischyrocerus_anguipes | II | 3 |
| lysianassidae_spp | I | 2 |
| maera_danae | I | 2 |
| monoculopsis_longicornis | II | 3 |
| orchomenella_minuta | II | 3 |
| phoxocephalus_holbolli | I | 3 |
| pontogeneia_inermis | II | 2 |
| pontoporeia_femorata | I | 3 |
| protomedeia_fasciata | II | 3 |
| protomedeia_grandimana | II | 3 |
| quasimelita_formosa | I | 2 |
| quasimelita_quadrispinosa | I | 3 |
When we considered the data without a distinction by station, the global BOPA index is 0.003443646.
3.10.3. Ecological Quality Status
To use the EQR classification, we used the conversion method from Dauvin & Ruellet (2007).
3.11. BenthoVal index
This index is a work-in-progress by the team of Céline Labrune and Olivier Gauthier at IFREMER. This pressure score still needs to be enhanced so that more human activities are included and the score is better defined.
4. Relationships between indicators and abiotic parameters
In this section, we study the statistical relationships between indicators calculated above and different abiotic parameters, in order to understand how well they can be used to detect perturbations.
4.1. Covariation
Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.
⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.
Richness
Density
Biomass
Diversity
Evenness
Taxonomic diversity
Functional richness
Functional divergence
Functional evenness
AMBI
M_AMBI
BOPA
BENTIX
4.2. Correlation
Correlations have been calculated with Spearman’s rank coefficients.
| S | N | B | H | J | delta | FR | FD | FE | AMBI | M_AMBI | BOPA | BENTIX | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| om | -0.026 | -0.121 | 0.074 | 0.122 | 0.136 | 0.059 | -0.109 | 0.066 | -0.034 | -0.167 | 0.094 | 0.184 | 0.305 |
| gravel | 0.029 | 0.007 | 0.126 | 0.012 | 0.07 | 0.074 | 0.242 | -0.139 | 0.079 | 0.054 | -0.005 | -0.017 | -0.17 |
| sand | 0.059 | 0.084 | -0.078 | 0.031 | -0.027 | 0.081 | 0.08 | 0.075 | 0.049 | 0.199 | -0.018 | -0.28 | -0.305 |
| silt | -0.054 | -0.013 | 0.05 | -0.068 | -0.055 | -0.139 | -0.117 | 0.002 | -0.091 | -0.17 | 0.007 | 0.301 | 0.279 |
| clay | -0.098 | -0.076 | -0.071 | -0.044 | 0.02 | -0.059 | 0.028 | -0.08 | -0.067 | -0.05 | -0.06 | 0.07 | 0.02 |
| arsenic | -0.266 | -0.149 | -0.13 | -0.193 | 0.008 | -0.125 | -0.275 | 0.017 | -0.137 | -0.036 | -0.196 | 0.266 | 0.136 |
| cadmium | -0.308 | -0.042 | -0.133 | -0.291 | -0.133 | -0.275 | -0.294 | 0.163 | -0.246 | -0.019 | -0.279 | 0.237 | 0.084 |
| chromium | -0.331 | -0.167 | -0.106 | -0.273 | -0.041 | -0.222 | -0.353 | 0.056 | -0.173 | -0.021 | -0.283 | 0.287 | 0.163 |
| copper | -0.298 | -0.172 | -0.123 | -0.225 | -0.025 | -0.199 | -0.336 | 0.109 | -0.171 | -0.018 | -0.255 | 0.252 | 0.183 |
| iron | -0.377 | -0.273 | -0.017 | -0.251 | 0.034 | -0.171 | -0.385 | 0.057 | -0.112 | 0.004 | -0.303 | 0.248 | 0.09 |
| manganese | -0.287 | -0.096 | -0.076 | -0.261 | -0.085 | -0.245 | -0.314 | 0.069 | -0.23 | -0.031 | -0.252 | 0.333 | 0.162 |
| mercury | -0.234 | -0.084 | -0.002 | -0.199 | -0.075 | -0.228 | -0.307 | 0.145 | -0.194 | -0.043 | -0.184 | 0.269 | 0.169 |
| lead | -0.304 | -0.135 | -0.117 | -0.252 | -0.051 | -0.216 | -0.312 | 0.097 | -0.197 | 0.007 | -0.264 | 0.301 | 0.124 |
| zinc | -0.32 | -0.145 | -0.12 | -0.253 | -0.056 | -0.221 | -0.336 | 0.161 | -0.188 | -0.01 | -0.274 | 0.26 | 0.15 |
| S | N | B | H | J | delta | FR | FD | FE | AMBI | M_AMBI | BOPA | BENTIX | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| om | 0.7858 | 0.2113 | 0.4466 | 0.2093 | 0.1614 | 0.544 | 0.2628 | 0.4963 | 0.7284 | 0.08394 | 0.3319 | 0.05618 | 0.001327 |
| gravel | 0.7662 | 0.9425 | 0.1952 | 0.9048 | 0.4692 | 0.4459 | 0.01174 | 0.1513 | 0.4148 | 0.5795 | 0.9576 | 0.8576 | 0.07776 |
| sand | 0.5414 | 0.3846 | 0.4196 | 0.752 | 0.7828 | 0.4045 | 0.4099 | 0.4429 | 0.6168 | 0.03891 | 0.8558 | 0.00334 | 0.001331 |
| silt | 0.581 | 0.8963 | 0.6099 | 0.4834 | 0.5692 | 0.1509 | 0.2264 | 0.9844 | 0.3508 | 0.07875 | 0.9425 | 0.001537 | 0.003456 |
| clay | 0.3134 | 0.4336 | 0.4634 | 0.6486 | 0.8392 | 0.5453 | 0.7737 | 0.4117 | 0.4939 | 0.6054 | 0.5407 | 0.4685 | 0.8405 |
| arsenic | 0.005376 | 0.1238 | 0.1814 | 0.04503 | 0.9356 | 0.1991 | 0.003949 | 0.8603 | 0.1586 | 0.7147 | 0.04233 | 0.005411 | 0.1592 |
| cadmium | 0.001171 | 0.6656 | 0.171 | 0.002263 | 0.1701 | 0.00396 | 0.002036 | 0.09262 | 0.01018 | 0.8475 | 0.003504 | 0.01348 | 0.3893 |
| chromium | 0.0004702 | 0.08459 | 0.273 | 0.004269 | 0.6766 | 0.02095 | 0.0001797 | 0.5643 | 0.07347 | 0.8322 | 0.003027 | 0.002598 | 0.09147 |
| copper | 0.001731 | 0.07495 | 0.2053 | 0.0195 | 0.8009 | 0.03895 | 0.0003739 | 0.2608 | 0.07738 | 0.8572 | 0.007774 | 0.008463 | 0.05769 |
| iron | 5.82e-05 | 0.004321 | 0.8628 | 0.00892 | 0.725 | 0.07715 | 3.87e-05 | 0.5548 | 0.2485 | 0.9679 | 0.001522 | 0.009583 | 0.3544 |
| manganese | 0.00256 | 0.3224 | 0.4366 | 0.006285 | 0.3814 | 0.01088 | 0.0009191 | 0.4765 | 0.01642 | 0.7532 | 0.008646 | 0.0004345 | 0.09382 |
| mercury | 0.0146 | 0.3891 | 0.9837 | 0.03882 | 0.4404 | 0.01764 | 0.001231 | 0.1336 | 0.04375 | 0.6622 | 0.05723 | 0.004795 | 0.07972 |
| lead | 0.001371 | 0.1643 | 0.2268 | 0.00859 | 0.5997 | 0.02485 | 0.00101 | 0.3171 | 0.04112 | 0.9397 | 0.005709 | 0.001546 | 0.2018 |
| zinc | 0.0007423 | 0.1333 | 0.2148 | 0.00819 | 0.5628 | 0.02152 | 0.0003788 | 0.09615 | 0.0519 | 0.9183 | 0.004098 | 0.00654 | 0.1206 |